code: def _transpose(cls, arg0, dim0, dim1):
      # TODO: check if need to return this or not
      if not (dim0 == 1 or dim0 == -2):
          return NotImplemented
      if not (dim1 == 2 or dim1 == -1):
          return NotImplemented
  
      B, m, n = arg0.shape
      values = arg0.__values
  
      (
          output_row_indices,
          output_values,
          output_row_offsets,
          output_column_indices,
      ) = _transpose_with_info(values, arg0.__transp_info)
      new_transp_info = _get_transpose_info(
          n,
          m,
          output_row_indices,
          output_row_offsets,
          output_column_indices,
      )
  
      return cls._wrap(
          (B, n, m),
          output_values,
          output_row_indices,
          output_row_offsets,
          output_column_indices,
          new_transp_info,
      )
  
imports: import torch
  from xformers.ops import masked_matmul
  from xformers.sparse import _csr_ops
  from xformers.sparse.utils import _csr_to_coo, _dense3d_to_sparse, _diffsort, _get_transpose_info, _transpose_with_info
label_desc: Use image node_id 14 for calling the SparseCSRTensor obj's underlying member method code with example usage: obj._transpose(cls, arg0, dim0, dim1) and returns: cls, NotImplemented, NotImplemented